Method and apparatus for processing image

ABSTRACT

Embodiments of the present disclosure provide a method and apparatus for processing an image, and relates to the field of computer vision technology. The method may include: acquiring a value to be processed, where the value to be processed is associated with an image to be processed; and processing the value to be processed by using a quality scoring model to generate a score of the image to be processed in a target scoring domain, where the score of the image to be processed in the target scoring domain is related to an image quality of the image to be processed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No.202010325574.1, filed on Apr. 23, 2020, titled “Method and apparatus forprocessing image,” which is hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate to the field of computertechnology, specifically to the field of computer vision technology, andparticularly to a method and apparatus for processing an image.

BACKGROUND

With the development of Internet technology, various Internet platformshave gradually emerged, such as video websites and live broadcastplatforms. When watching a recorded video or live broadcast, the qualityof an image may directly affect user's watching experience. For example,high image quality can help a user obtain a better watching experience,while poor image quality may cause the user to give up watching.

The user's evaluation on the image is a subjective evaluation, sodifferent users may have different evaluations on the quality of thesame image.

SUMMARY

A method and apparatus for processing an image, an electronic device,and a storage medium are provided.

According to a first aspect, an embodiment of the present disclosureprovides a method for processing an image. The method includes:acquiring a value to be processed, where the value to be processed isassociated with an image to be processed; and processing the value to beprocessed by using a quality scoring model to generate a score of theimage to be processed in a target scoring domain, where the score of theimage to be processed in the target scoring domain is related to animage quality of the image to be processed.

According to a second aspect, an embodiment of the present disclosureprovides an apparatus for processing an image. The apparatus includes:an acquisition unit, configured to acquire a value to be processed,where the value to be processed is associated with an image to beprocessed; and a generation unit, configured to process the value to beprocessed by using a quality scoring model to generate a score of theimage to be processed in a target scoring domain, where the score of theimage to be processed in the target scoring domain is related to animage quality of the image to be processed.

According to a third aspect, an embodiment of the present disclosureprovides an electronic device, including: one or more processors; and astorage apparatus for storing one or more programs, The one or moreprograms, when executed by the one or more processors, cause the one ormore processors to implement any embodiment of the method for processingan image.

According to a fourth aspect, an embodiment of the present disclosureprovides a computer-readable storage medium, storing a computer programthereon. The computer program, when executed by a processor, causes theprocessor to implement any embodiment of the method for processing animage.

BRIEF DESCRIPTION OF THE DRAWINGS

After reading detailed descriptions of non-limiting embodiments withreference to following accompanying drawings, other features, objectivesand advantages of the present disclosure will become more apparent.

FIG. 1 is an example system architecture diagram to which someembodiments of the present disclosure can be applied;

FIG. 2 is a flowchart of a method for processing an image according toan embodiment of the present disclosure;

FIG. 3 is a schematic diagram of an application scenario of the methodfor processing an image according to an embodiment of the presentdisclosure;

FIG. 4 is a flowchart of a method for processing an image according toanother embodiment of the present disclosure;

FIG. 5 is a schematic structural diagram of an apparatus for processingan image according to an embodiment of the present disclosure;

FIG. 6 is a block diagram of an electronic device used to implement themethod for processing an image according to embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

Example embodiments of the present disclosure will be described below incombination with accompanying drawings, which include various details ofembodiments of the present disclosure to facilitate understanding andshould be regarded as examples. Therefore, it should be appreciated bythose of ordinary skill in the art that various changes andmodifications can be made to the embodiments described here withoutdeparting from the scope and spirit of the present disclosure. Likewise,for clarity and conciseness, descriptions of well-known functions andstructures are omitted in the following description.

It should be noted that the embodiments in the present disclosure andthe features in the embodiments may be combined with each other withoutconflicts. The present disclosure will be described below in detail withreference to the accompanying drawings and in combination with theembodiments.

FIG. 1 shows an example system architecture 100 to which a method forprocessing an image or an apparatus for processing an image according tosome embodiments of the present disclosure may be applied.

As shown in FIG. 1 , the system architecture 100 may include terminaldevices 101, 102, and 103, a network 104, and a server 105. The network104 is used to provide a medium for communication links between theterminal devices 101, 102, and 103 and the server 105. The network 104may include various types of connections, such as wired or wirelesscommunication links, or optical fiber cables.

A user may use the terminal devices 101, 102, and 103 to interact withthe server 105 through the network 104 to receive or send messages andthe like. The terminal devices 101, 102, and 103 may be installed withvarious communication client applications, such as video applications,live broadcast applications, instant messaging tools, E-mail clients,and social platform software.

The terminal devices 101, 102, and 103 here may be hardware or software.When the terminal devices 101, 102, and 103 are hardware, they may bevarious electronic devices with display screens, including but notlimited to a smart phone, a tablet computer, an e-book reader, a laptopportable computer, a desktop computer, and the like. When the terminaldevices 101, 102, and 103 are software, they may be installed in theabove-listed electronic devices. The terminal devices 101, 102, and 103may be implemented as a plurality of software or software modules (forexample, a plurality pieces of software or a plurality of softwaremodules used to provide distributed services), or as single piece ofsoftware or single software modules. Specific limitations are not givenhere.

The server 105 may be a server providing various services, for example,a background server providing supports for the terminal devices 101,102, and 103. The background server may process, such as analyze, areceived value to be processed, and feed back a processing result (forexample, a score of an image to be processed in a target scoring domain)to the terminal device.

It should be noted that the method for processing an image according toembodiments of the present disclosure may be executed by the server 105or the terminal devices 101, 102, or 103, and accordingly, the apparatusfor processing an image may be provided in the server 105 or theterminal device 101, terminal device 102, or terminal device 103.

It should be understood that the numbers of the terminal devices, thenetwork, and the server in FIG. 1 are merely illustrative. Any number ofterminal devices, networks and servers may be configured according toactual requirements.

Continuing to refer to FIG. 2 , a flow 200 of a method for processing animage according to an embodiment of the present disclosure is shown. Themethod for processing an image includes the following steps.

Step 201: acquiring a value to be processed. The value to be processedis associated with an image to be processed.

In this embodiment, the executing body (for example, the server or theterminal device shown in FIG. 1 ) on which the method for processing animage runs may acquire the value to be processed. Specifically, thevalue to be processed is associated with the image to be processed. Forexample, the value to be processed may be generated based on the imageto be processed. For example, the value to be processed corresponding tothe image to be processed is generated by means of a presetcorresponding relationship (such as a preset model or a presetcorresponding relationship table) between the image to be processed andthe value to be processed. In addition, the value to be processed mayalso be an attribute value of the image to be processed, such asdefinition. The image in an embodiment of the present disclosure may bea video frame of a video, or a non-video frame, that is, a single image.

In practice, the executing body may directly acquire the value to beprocessed from the local or other electronic device, or may firstacquire the image to be processed and then generate the value to beprocessed in real time by using the image to be processed.

Step 202: processing the value to be processed by using a qualityscoring model to generate a score of the image to be processed in atarget scoring domain. The score of the image to be processed in thetarget scoring domain is related to an image quality of the image to beprocessed.

In this embodiment, the executing body may process the value to beprocessed by using the quality scoring model, so as to obtain the scoreoutput from the quality scoring model. In practice, the executing bodymay input the value to be processed into the quality scoring model togenerate the score. In addition, the executing body may also continue toprocess the value to be processed by using the quality scoring modelafter acquiring the value to be processed by means of the qualityscoring model.

The above-mentioned score is a score of the image to be processed in thetarget scoring domain, that is, a target domain. The quality scoringmodel is a model for generating, for an image, a score related to animage quality. In the same scoring domain, the higher the score of animage is, the better the image quality is. Image quality influencingfactors may include at least one item, such as definition, and may alsoinclude aesthetics, contrast and/or brightness of a target and abackground, and the like. The score of the image quality of an image isaffected by one or more quality influencing factors of the image. Scoresof each scoring domain may have a corresponding value range, and thevalue ranges of scores of different scoring domains may be the same ordifferent. When the image quality of an image is within a preset qualityrange, the quality influencing factors of the image have differenteffects on the score of the image in different scoring domains. Thepreset quality range here may be a general image quality that is neithera high image quality nor a poor image quality.

For example, there are three images A, B, and C. The definition of thethree images sequentially increase, and are respectively a very blurryvideo, a slightly blurry video, and a clear video. The value range of afirst scoring domain is 0-100 scores, and the scores of the three imagesare respectively 1, 2, and 8 scores. The value range of a second scoringdomain is 0-10 scores, and the scores of the three images arerespectively 10, 50, and 80 scores. The influence of definition on thescores in the first scoring domain is greater than that in the secondscoring domain.

In practice, the quality scoring model may be, for example, variousfunctions and deep neural networks such as convolutional neural networksused to characterize the corresponding relationship between the value tobe processed of the image to be processed and the score of the image.

According to the method provided by the embodiments of the presentdisclosure, a score used to characterize an image quality in a targetscoring domain may be acquired from the value to be processed, therebyenriching the acquisition form of the score. In addition, the accuracyof generating the score may be improved by means of a quality scoringmodel.

In some optional implementations of this embodiment, the value to beprocessed is a score of the image to be processed in an original scoringdomain; and step 202 may include: inputting the score of the image to beprocessed in the original scoring domain into the quality scoring modelto obtain the score of the image to be processed in the target scoringdomain, where the quality scoring model is a monotonic neural network,and the number of hidden units in the monotonic neural network issmaller than a preset threshold.

In these optional implementations, the executing body may acquire thescore of the image to be processed in the original scoring domain, andinput the score into the quality scoring model to obtain the scoreoutput from the quality scoring model. The score output here is thescore of the image to be processed in the target scoring domain. Theoriginal scoring domain and the target scoring domain may be any twodifferent scoring domains, and the quality scoring model is used togenerate the score in the target scoring domain from the score in theoriginal scoring domain. The hidden units may be units other than aninput unit and an output unit in the monotonic neural network, such asfully connected layers. For example, the number of fully connectedlayers in the monotonic neural network may be smaller than the number offully connected layers in a conventional monotonic neural network. Forexample, in the case of three fully connected layers in the conventionalmonotonic neural network, the monotonic neural network may only retainone or two fully connected layers.

Specifically, the quality scoring model may be a monotonic neuralnetwork, and may also be a monotonic increasing function. Therelationship between the output and the input of the monotonic neuralnetwork may be a monotonic increasing relationship. For example, themonotonic neural network may be an unconstrained monotonic neuralnetwork.

Optionally, the monotonic neural network may be obtained by trainingthrough the following steps: acquiring a plurality of training samples,where a training sample among the plurality of training samples includesthe score of the image in the original scoring domain, and a referencescore of the image in the target scoring domain; then, inputting thescore of the image in the original scoring domain in the training sampleinto an initial monotonic neural network to obtain a predicted score,output from the initial monotonic neural network, of the image in thetarget scoring domain; and finally, inputting the predicted score andthe reference score in the training sample into a preset loss functionto obtain a loss value, and performing training by means of the lossvalue, for example, performing back propagation in the monotonic neuralnetwork, to obtain the trained monotonic neural network. The executingbody of the training process may be the above-mentioned executing bodyor other electronic device. The initial monotonic neural network refersto a monotonic neural network to be trained.

The above-mentioned executing body or other electronic device mayautomatically learn the monotonicity between the score in the originalscoring domain and the score in the target scoring domain through thetraining sample, which also improves the accuracy of the generated scorein the target scoring domain.

These implementations may directly generate the score of the image inanother scoring domain from the score of the image in one scoring domainby means of the quality scoring model, thereby realizing the conversionof scores between different scoring domains. Moreover, theseimplementations may learn to obtain a monotonic neural network withoutobtaining a large number of samples in the target scoring domain, whichimproves the learning efficiency. In addition, the monotonic neuralnetwork uses fewer hidden units, which may improve the efficiency of themonotonic neural network.

Continue to refer to FIG. 3 , which is a schematic diagram of anapplication scenario of the method for processing an image according toan embodiment. In the application scenario of FIG. 3 , an execution body301 acquires a value to be processed 302. The value to be processed isassociated with the image to be processed, for example, the value to beprocessed is generated based on the image to be processed. The executionbody 301 processes the value to be processed 302 by using a qualityscoring model 303 to generate a score 304 of the image to be processedin a target scoring domain. The score of the image to be processed inthe target scoring domain is related to an image quality of the image tobe processed.

Further referring to FIG. 4 , a flow 400 of an embodiment of the methodfor processing an image is shown. The quality scoring model may includea scoring network and a monotonic neural network. The flow 400 includesthe following steps.

Step 401: inputting the image to be processed into the scoring networkto obtain an initial score, output from the scoring network, of theimage to be processed.

In this embodiment, the executing body (such as the server or terminaldevice shown in FIG. 1 ) on which the method for processing an imageruns may input the image to be processed into the scoring network toobtain the initial score of the image to be processed. The initial scoreis output from the scoring network.

The scoring network here may be any deep neural network that maygenerate a value associated with the image from the image, such as aconvolutional neural network, or a residual neural network.Specifically, the initial score here is not a score in any scoringdomain, but only an intermediate value used to generate a score in acertain scoring domain.

Step 402: inputting the initial score into the monotonic neural networkto obtain a score of the image to be processed in a target scoringdomain. The number of hidden units in the monotonic neural network issmaller than a preset threshold.

In this embodiment, the above-mentioned executing body may input theinitial score into the monotonic neural network to obtain the score ofthe image to be processed in the target scoring domain. The scoreobtained here is output from the monotonic neural network. The monotonicneural network may obtain, from the initial score of the image to beprocessed, the score of the image to be processed in the target scoringdomain.

A training sample for the quality scoring model includes: an image and areference score of the image in a domain corresponding to a targetdomain identifier. Specifically, the training steps may include:inputting the image in the training sample into a scoring network in aninitial quality scoring model to obtain an initial score output by thescoring network; inputting the initial score into a monotonic neuralnetwork in the initial quality scoring model to obtain a score, outputby the monotonic neural network, of the image in the target scoringdomain; and then, inputting the score and the reference score into apreset loss function to obtain a loss value, and training the initialquality scoring model by means of the loss value, for example,performing back propagation, to obtain a trained initial quality scoringmodel.

The training steps may be executed by the above-mentioned executing bodyor other electronic device. In this way, the executing body or otherelectronic device may perform joint training by using the scoringnetwork and the monotonic neural network, thereby obtaining the qualityscoring model that may accurately determine the score in the targetscoring domain.

In this embodiment, the accurate score of the image to be processed inthe target scoring domain may be generated by means of both the scoringnetwork and the monotonic neural network.

In some optional implementations of this embodiment, the quality scoringmodel includes at least two monotonic neural networks, and differentmonotonic neural networks in the at least two monotonic neural networkscorrespond to different scoring domains; and step 402 may include:inputting the initial score into the at least two monotonic neuralnetworks to obtain a score, output from each of the at least twomonotonic neural networks, of the image to be processed in a scoringdomain corresponding to the monotonic neural network.

In these optional implementations, the number of monotonic neuralnetworks included in the quality scoring model is at least two. Theexecuting body inputs the initial score into the at least two monotonicneural networks to obtain a score output from each of the at least twomonotonic neural networks. The number of target scoring domains is atleast two, and each output score corresponds to a target scoring domain.

These implementations may efficiently generate scores of the image in aplurality of scoring domains by means of the quality scoring model thatcombines a plurality of monotonic neural networks.

In some optional implementations of this embodiment, the method mayfurther include: acquiring a training sample set, where a trainingsample in the training sample set includes a sample image and areference score of the sample image in a specified scoring domain, andthe specified scoring domain and the target scoring domain are differentscoring domains; inputting the sample image into the scoring network toobtain an initial score of the sample image; inputting the initial scoreof the sample image into a monotonic neural network to be trained toobtain a predicted score of the sample image in the specified scoringdomain; and determining a loss value of the predicted score based on thereference score and the predicted score, and training the monotonicneural network to be trained by means of the loss value to obtain atrained monotonic neural network.

In these optional implementations, the executing body may separatelytrain the monotonic neural network to be trained, that is, train themonotonic neural network by using the initial score output by thescoring network. The reference score may be considered as a true valueof the sample image in the specified scoring domain. Specifically, theexecuting body may input the reference score and the predicted scoreinto a preset loss function to obtain the loss value, and performtraining by using the loss value, for example, perform back propagationin the monotonic neural network, to obtain the trained monotonic neuralnetwork.

By means of training in these implementation, the executing body orother electronic equipment may efficiently train the monotonic neuralnetwork that may generate a score in any specified scoring domain.

Further referring to FIG. 5 , as an implementation of the methods shownin the above figures, an embodiment of the present disclosure providesan apparatus for processing an image. The apparatus embodimentcorresponds to the method embodiment shown in FIG. 2 , in addition tothe features described below, the apparatus embodiment may also includethe same or corresponding features or effects as the method embodimentshown in FIG. 2 . The apparatus may be applied to various electronicdevices.

As shown in FIG. 5 , the apparatus 500 for processing an image in thisembodiment includes: an acquisition unit 501 and a generation unit 502.The acquisition unit 501 is configured to acquire a value to beprocessed. The value to be processed is associated with an image to beprocessed. The generation unit 502 is configured to process the value tobe processed by using a quality scoring model to generate a score of theimage to be processed in a target scoring domain. The score of the imageto be processed in the target scoring domain is related to an imagequality of the image to be processed.

In this embodiment, the specific processing of the acquisition unit 501and the generation unit 502 of the apparatus 500 for processing an imageand the technical effects brought accordingly may be referred to therelevant descriptions of step 201 and step 202 in the embodimentcorresponding to FIG. 2 , and details are not described herein again.

In some optional implementations of this embodiment, the value to beprocessed is a score of the image to be processed in an original scoringdomain; and the generation unit is further configured to process thevalue to be processed by using the quality scoring model to generate thescore of the image to be processed in the target scoring domain by:inputting the score of the image to be processed in the original scoringdomain into the quality scoring model to obtain the score of the imageto be processed in the target scoring domain, where the quality scoringmodel is a monotonic neural network, and the number of hidden units inthe monotonic neural network is smaller than a preset threshold.

In some optional implementation modes of this embodiment, the qualityscoring model includes a scoring network and a monotonic neural network.The acquisition unit is further configured to acquire the value to beprocessed by: inputting the image to be processed into the scoringnetwork to obtain an initial score, output by the scoring network, ofthe image to be processed; and the generation unit is further configuredto process the value to be processed by using the quality scoring modelto generate the score of the image to be processed in the target scoringdomain by: inputting the initial score into the monotonic neural networkto obtain the score of the image to be processed in the target scoringdomain, where the number of hidden units in the monotonic neural networkis smaller than a preset threshold.

In some optional implementations of this embodiment, the quality scoringmodel includes at least two monotonic neural networks, and differentmonotonic neural networks in the at least two monotonic neural networkscorrespond to different scoring domains; and the generation unit isfurther configured to input the initial score into the monotonic neuralnetwork to obtain the score of the image to be processed in the targetscoring domain by: inputting the initial score into the at least twomonotonic neural networks to obtain a score, output from each of the atleast two monotonic neural networks, of the image to be processed in ascoring domain corresponding to the monotonic neural network.

In some optional implementations of this embodiment, the apparatusfurther includes: a sample acquisition unit, configured to acquire atraining sample set, where a training sample in the training sample setincludes a sample image and a reference score of the sample image in aspecified scoring domain, and the specified scoring domain and thetarget scoring domain are different scoring domains; an input unit,configured to input the sample image into the scoring network to obtainan initial score of the sample image; a prediction unit, configured toinput the initial score of the sample image into a monotonic neuralnetwork to be trained to obtain a predicted score of the sample image inthe specified scoring domain; and a determination unit, configured todetermine a loss value of the predicted score based on the referencescore and the predicted score, and train the monotonic neural network tobe trained by means of the loss value to obtain a trained monotonicneural network.

Embodiments of the present disclosure further provide an electronicdevice and a readable storage medium.

As shown in FIG. 6 , which is a block diagram of an electronic device ofa method for processing an image according to an embodiment of thepresent disclosure. The electronic device is intended to representvarious forms of digital computers, such as laptop computers, desktopcomputers, workbenches, personal digital assistants, servers, bladeservers, mainframe computers, and other suitable computers. Theelectronic device may also represent various forms of mobileapparatuses, such as personal digital processing, cellular phones, smartphones, wearable devices, and other similar computing apparatuses. Thecomponents shown herein, their connections and relationships, and theirfunctions are merely examples, and are not intended to limit theimplementation of the present disclosure described and/or claimedherein.

As shown in FIG. 6 , the electronic device includes: one or moreprocessors 601, a memory 602, and interfaces for connecting variouscomponents, including high-speed interfaces and low-speed interfaces.The various components are connected to each other using differentbuses, and may be installed on a common motherboard or in other methodsas needed. The processor may process instructions executed within theelectronic device, including instructions stored in or on the memory todisplay graphic information of GUI on an external input/output apparatus(such as a display device coupled to the interface). In otherembodiments, a plurality of processors and/or a plurality of buses maybe used together with a plurality of memories if desired. Similarly, aplurality of electronic devices may be connected, and the devicesprovide some necessary operations (for example, as a server array, a setof blade servers, or a multi-processor system). In FIG. 6 , oneprocessor 601 is used as an example.

The memory 602 is a non-transitory computer readable storage mediumprovided by an embodiment of the present disclosure. The memory storesinstructions executable by at least one processor, so that the at leastone processor performs the method for processing a video frame providedby an embodiment of the present disclosure. The non-transitory computerreadable storage medium of an embodiment of the present disclosurestores computer instructions for causing a computer to perform themethod for processing an image provided by the embodiment of the presentdisclosure.

The memory 602, as a non-transitory computer readable storage medium,may be used to store non-transitory software programs, non-transitorycomputer executable programs and modules, such as programinstructions/modules corresponding to the method for processing a videoframe in the embodiments of the present disclosure (for example, theacquisition unit 501, and the generation unit 502 shown in FIG. 5 ). Theprocessor 601 executes the non-transitory software programs,instructions, and modules stored in the memory 602 to execute variousfunctional applications and data processing of the server, that is, toimplement the method for processing an image in the foregoing methodembodiment.

The memory 602 may include a storage program area and a storage dataarea, where the storage program area may store an operating system andat least one function required application program; and the storage dataarea may store data created by the use of the electronic deviceaccording to the method for processing parking, etc. In addition, thememory 602 may include a high-speed random access memory, and may alsoinclude a non-transitory memory, such as at least one magnetic diskstorage device, a flash memory device, or other non-transitorysolid-state storage devices. In some embodiments, the memory 602 mayoptionally include memories remotely provided with respect to theprocessor 601, and these remote memories may be connected to theelectronic device of the method for processing parking through anetwork. Examples of the above network include but are not limited tothe Internet, intranet, local area network, mobile communicationnetwork, and combinations thereof.

The electronic device of the method for processing an image may furtherinclude: an input apparatus 603 and an output apparatus 604. Theprocessor 601, the memory 602, the input apparatus 603, and the outputapparatus 604 may be connected through a bus or in other methods. InFIG. 6 , connection through a bus is used as an example.

The input apparatus 603 may receive input digital or characterinformation, and generate key signal inputs related to user settings andfunction control of the electronic device of the method for processingparking, such as touch screen, keypad, mouse, trackpad, touchpad,pointing stick, one or more mouse buttons, trackball, joystick and otherinput apparatuses. The output apparatus 604 may include a displaydevice, an auxiliary lighting apparatus (for example, LED), a tactilefeedback apparatus (for example, a vibration motor), and the like. Thedisplay device may include, but is not limited to, a liquid crystaldisplay (LCD), a light emitting diode (LED) display, and a plasmadisplay. In some embodiments, the display device may be a touch screen.

Various embodiments of the systems and technologies described herein maybe implemented in digital electronic circuit systems, integrated circuitsystems, dedicated ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various embodiments may include: being implemented in one or morecomputer programs that can be executed and/or interpreted on aprogrammable system that includes at least one programmable processor.The programmable processor may be a dedicated or general-purposeprogrammable processor, and may receive data and instructions from astorage system, at least one input apparatus, and at least one outputapparatus, and transmit the data and instructions to the storage system,the at least one input apparatus, and the at least one output apparatus.

These computing programs (also referred to as programs, software,software applications, or codes) include machine instructions of theprogrammable processor and may use high-level processes and/orobject-oriented programming languages, and/or assembly/machine languagesto implement these computing programs. As used herein, the terms“machine readable medium” and “computer readable medium” refer to anycomputer program product, device, and/or apparatus (for example,magnetic disk, optical disk, memory, programmable logic apparatus (PLD))used to provide machine instructions and/or data to the programmableprocessor, including machine readable medium that receives machineinstructions as machine readable signals. The term “machine readablesignal” refers to any signal used to provide machine instructions and/ordata to the programmable processor.

In order to provide interaction with a user, the systems andtechnologies described herein may be implemented on a computer, thecomputer has: a display apparatus for displaying information to the user(for example, CRT (cathode ray tube) or LCD (liquid crystal display)monitor); and a keyboard and a pointing apparatus (for example, mouse ortrackball), and the user may use the keyboard and the pointing apparatusto provide input to the computer. Other types of apparatuses may also beused to provide interaction with the user; for example, feedbackprovided to the user may be any form of sensory feedback (for example,visual feedback, auditory feedback, or tactile feedback); and any form(including acoustic input, voice input, or tactile input) may be used toreceive input from the user.

The systems and technologies described herein may be implemented in acomputing system that includes backend components (e.g., as a dataserver), or a computing system that includes middleware components(e.g., application server), or a computing system that includes frontendcomponents (for example, a user computer having a graphical userinterface or a web browser, through which the user may interact with theimplementations of the systems and the technologies described herein),or a computing system that includes any combination of such backendcomponents, middleware components, or frontend components. Thecomponents of the system may be interconnected by any form or medium ofdigital data communication (e.g., communication network). Examples ofthe communication network include: local area networks (LAN), wide areanetworks (WAN), the Internet, and blockchain networks.

The computer system may include a client and a server. The client andthe server are generally far from each other and usually interactthrough the communication network. The relationship between the clientand the server is generated by computer programs that run on thecorresponding computer and have a client-server relationship with eachother.

Flowcharts and block diagrams in the drawings illustrate architectures,functionalities, and operations of possible implementations of systems,methods, and computer program products in accordance with variousembodiments of the present disclosure. In this regard, each block in aflowchart or block diagram may represent a module, program segment, orportion of code that contains one or more executable instructions forimplementing a specified logical functionality. It should also be notedthat in some alternative implementations, the functionalities noted inthe blocks may also occur in an order different from that noted in thedrawings. For example, two successively represented blocks may actuallybe executed substantially in parallel, and they may sometimes beexecuted in the reverse order, depending on the functionality involved.It is also noted that each block of the block diagrams and/orflowcharts, and combinations of blocks in the block diagrams and/orflowcharts, may be implemented with a dedicated hardware-based systemthat performs the specified functions or operations, or may beimplemented with a combination of dedicated hardware and computerinstructions.

The units involved in embodiments of the present disclosure may beimplemented by software, or may be implemented by hardware. Thedescribed units may also be provided in a processor, for example,described as: a processor including an acquisition unit, and ageneration unit. In some cases, the names of these units do notconstitute a limitation to such units themselves. For example, theacquisition unit may also be described as “a unit configured to acquirea value to be processed.”

In another aspect, an embodiment of the present disclosure furtherprovides a computer-readable medium. The computer-readable medium may beincluded in the apparatus described in the above embodiments, or astand-alone computer-readable medium without being assembled into theapparatus. The computer-readable medium carries one or more programs.The one or more programs, when executed by the apparatus, cause theapparatus to: acquire a value to be processed, where the value to beprocessed is associated with an image to be processed; and process thevalue to be processed by using a quality scoring model to generate ascore of the image to be processed in a target scoring domain, where thescore of the image to be processed in the target scoring domain isrelated to an image quality of the image to be processed.

The above description is an example embodiment of the disclosure and adescription of the technical principles employed. It should beunderstood by those skilled in the art that the scope of the inventionreferred to in this disclosure is not limited to the technical solutionsformed by specific combinations of the above-mentioned technicalfeatures, but also covers other technical solutions formed by anycombination of the above-mentioned technical features or equivalentsthereof without departing from the inventive concept. For example, theabove-mentioned features and the technical features having similarfunctionalities disclosed in the present disclosure are replaced witheach other.

What is claimed is:
 1. A method for processing an image, the methodcomprising: acquiring a value to be processed, wherein the value to beprocessed is associated with an image to be processed; and processingthe value to be processed by using a quality scoring model to generate ascore of the image to be processed in a target scoring domain, whereinthe score of the image to be processed in the target scoring domain isrelated to an image quality of the image to be processed; wherein thevalue to be processed is a score of the image to be processed in anoriginal scoring domain; and processing the value to be processed byusing the quality scoring model to generate the score of the image to beprocessed in the target scoring domain comprises: inputting the score ofthe image to be processed in the original scoring domain into thequality scoring model to obtain the score of the image to be processedin the target scoring domain, wherein the quality scoring model is amonotonic neural network, and a number of hidden units in the monotonicneural network is smaller than a preset threshold.
 2. The methodaccording to claim 1, wherein the quality scoring model comprises ascoring network and a monotonic neural network; and acquiring the valueto be processed comprises: inputting the image to be processed into thescoring network to obtain an initial score, output from the scoringnetwork, of the image to be processed; and processing the value to beprocessed by using the quality scoring model to generate the score ofthe image to be processed in the target scoring domain comprises:inputting the initial score into the monotonic neural network to obtainthe score of the image to be processed in the target scoring domain,wherein a number of hidden units in the monotonic neural network issmaller than a preset threshold.
 3. The method according to claim 2,wherein the quality scoring model comprises at least two monotonicneural networks, and different monotonic neural networks in the at leasttwo monotonic neural networks correspond to different scoring domains;and inputting the initial score into the monotonic neural network toobtain the score of the image to be processed in the target scoringdomain comprises: inputting the initial score into the at least twomonotonic neural networks to obtain a score, output from each of the atleast two monotonic neural networks, of the image to be processed in ascoring domain corresponding to the monotonic neural network.
 4. Themethod according to claim 2, wherein the method further comprises:acquiring a training sample set, wherein a training sample in thetraining sample set comprises a sample image and a reference score ofthe sample image in a specified scoring domain, and the specifiedscoring domain and the target scoring domain are different scoringdomains; inputting the sample image into the scoring network to obtainan initial score of the sample image; inputting the initial score of thesample image into a monotonic neural network to be trained to obtain apredicted score of the sample image in the specified scoring domain; anddetermining a loss value of the predicted score based on the referencescore and the predicted score, and training the monotonic neural networkto be trained by means of the loss value to obtain a trained monotonicneural network.
 5. An electronic device, comprising: one or moreprocessors; and a memory for storing one or more programs, wherein theone or more programs, when executed by the one or more processors, causethe one or more processors to perform operations, the operationscomprising: acquiring a value to be processed, wherein the value to beprocessed is associated with an image to be processed; and processingthe value to be processed by using a quality scoring model to generate ascore of the image to be processed in a target scoring domain, whereinthe score of the image to be processed in the target scoring domain isrelated to an image quality of the image to be processed; wherein thevalue to be processed is a score of the image to be processed in anoriginal scoring domain; and processing the value to be processed byusing the quality scoring model to generate the score of the image to beprocessed in the target scoring domain comprises: inputting the score ofthe image to be processed in the original scoring domain into thequality scoring model to obtain the score of the image to be processedin the target scoring domain wherein the quality scoring model is amonotonic neural network and a number of hidden units in the monotonicneural network is smaller than a preset threshold.
 6. The electronicdevice according to claim 5, wherein the quality scoring model comprisesa scoring network and a monotonic neural network; and acquiring thevalue to be processed comprises: inputting the image to be processedinto the scoring network to obtain an initial score, output from thescoring network, of the image to be processed; and processing the valueto be processed by using the quality scoring model to generate the scoreof the image to be processed in the target scoring domain comprises:inputting the initial score into the monotonic neural network to obtainthe score of the image to be processed in the target scoring domain,wherein a number of hidden units in the monotonic neural network issmaller than a preset threshold.
 7. The electronic device according toclaim 6, wherein the quality scoring model comprises at least twomonotonic neural networks, and different monotonic neural networks inthe at least two monotonic neural networks correspond to differentscoring domains; and inputting the initial score into the monotonicneural network to obtain the score of the image to be processed in thetarget scoring domain comprises: inputting the initial score into the atleast two monotonic neural networks to obtain a score, output from eachof the at least two monotonic neural networks, of the image to beprocessed in a scoring domain corresponding to the monotonic neuralnetwork.
 8. The electronic device according to claim 6, wherein theoperations further comprise: acquiring a training sample set, wherein atraining sample in the training sample set comprises a sample image anda reference score of the sample image in a specified scoring domain, andthe specified scoring domain and the target scoring domain are differentscoring domains; inputting the sample image into the scoring network toobtain an initial score of the sample image; inputting the initial scoreof the sample image into a monotonic neural network to be trained toobtain a predicted score of the sample image in the specified scoringdomain; and determining a loss value of the predicted score based on thereference score and the predicted score, and training the monotonicneural network to be trained by means of the loss value to obtain atrained monotonic neural network.
 9. A non-transitory computer-readablestorage medium, storing a computer program thereon, wherein the computerprogram, when executed by a processor, causes the processor to performoperations, the operations comprising: acquiring a value to beprocessed, wherein the value to be processed is associated with an imageto be processed; and processing the value to be processed by using aquality scoring model to generate a score of the image to be processedin a target scoring domain, wherein the score of the image to beprocessed in the target scoring domain is related to an image quality ofthe image to be processed; wherein the value to be processed is a scoreof the image to be processed in an original scoring domain; andprocessing the value to be processed by using the quality scoring modelto generate the score of the image to be processed in the target scoringdomain comprises: inputting the score of the image to be processed inthe original scoring domain into the quality scoring model to obtain thescore of the image to be processed in the target scoring domain, whereinthe quality model is a monotonic neural network, and a number of hiddenunits in the monotonic neural network is smaller than a presetthreshold.
 10. The non-transitory computer-readable storage mediumaccording to claim 9, wherein the quality scoring model comprises ascoring network and a monotonic neural network; and acquiring the valueto be processed comprises: inputting the image to be processed into thescoring network to obtain an initial score, output from the scoringnetwork, of the image to be processed; and processing the value to beprocessed by using the quality scoring model to generate the score ofthe image to be processed in the target scoring domain comprises:inputting the initial score into the monotonic neural network to obtainthe score of the image to be processed in the target scoring domain,wherein a number of hidden units in the monotonic neural network issmaller than a preset threshold.
 11. The non-transitorycomputer-readable storage medium according to claim 10, wherein thequality scoring model comprises at least two monotonic neural networks,and different monotonic neural networks in the at least two monotonicneural networks correspond to different scoring domains; and inputtingthe initial score into the monotonic neural network to obtain the scoreof the image to be processed in the target scoring domain comprises:inputting the initial score into the at least two monotonic neuralnetworks to obtain a score, output from each of the at least twomonotonic neural networks, of the image to be processed in a scoringdomain corresponding to the monotonic neural network.
 12. Thenon-transitory computer-readable storage medium according to claim 10,wherein the operations further comprise: acquiring a training sampleset, wherein a training sample in the training sample set comprises asample image and a reference score of the sample image in a specifiedscoring domain, and the specified scoring domain and the target scoringdomain are different scoring domains; inputting the sample image intothe scoring network to obtain an initial score of the sample image;inputting the initial score of the sample image into a monotonic neuralnetwork to be trained to obtain a predicted score of the sample image inthe specified scoring domain; and determining a loss value of thepredicted score based on the reference score and the predicted score,and training the monotonic neural network to be trained by means of theloss value to obtain a trained monotonic neural network.